The advent of the internet has significantly enhanced accessibility to information, facilitating the engagement of diverse communities with online resources. Despite the abundance of information available, navigating the structures of large organizations and effectively digesting essential personalized information remains a challenge. Consequently, individuals may be deterred from extracting valuable insights from already available resources. This paper addresses this issue by integrating a university's official website into an AI chatbot powered by large language models (LLMs). We demonstrate use cases to provide information tailored to general information-seeking and personalized information needs for college major selection. We present a novel approach for individuals to gain insights into large organizations via interactive conversation. Based on our system demonstration, we further delve into the role of generative AI in synthesizing vast organizational datasets into user-friendly formats accessible to the public and its implications for E-government and open government research.
This paper examines the use and theoretical support underlying the implementation of emerging technologies for citizen participation in cities. Based on a systematic literature review in the main database of digital government (Digital Government Research Library -DGRL-), we analyze the evidence drawn by prior research concerning the situation, theoretical support, and use of these disruptive technologies in citizen participation. We seek to contribute to prior research on arising a critical debate about the initial implementation of these technologies for promoting collaborative models of governance and providing future research directions to advance in this topic.
The transformation and development of the digital government serve as a crucial pathway for the modernization of national governance capability and level. Building a digital government is a key lever for the sustained development of Digital China in the era of big data. However, research indicates that the existing government smart service platforms, as one of the means for the modernization of digital government governance, still suffer from issues such as poor layout design and incomplete functional content, leading to a significant decrease in the willingness of the public users to use them. In light of this, the author primarily conducts an analysis of the theoretical framework and the current status of the construction and development of existing government smart service platforms through qualitative analysis, quantitative analysis, interdisciplinary research, etc. Based on this analysis, the study also includes research practices, problem elucidation, and cause analysis, along with platform construction practices, with the hope of exploring the development direction for the future upgrading and transformation of relevant smart service platforms.
Since its deployment in 2021, the Berlin Declaration monitoring mechanism (BDM) has proven to be an effective tool to monitor the digital transformation of the 27 Member States of the European Union (EU). Particularly, by putting forward seven Policy Areas that tackle different aspects of the digital sphere, the BDM has supported Member States in implementing the objectives set out in the Declaration, while fostering transparency and enabling countries to learn from one another. Overall, the results of the monitoring mechanism between 2021 and 2022 vary depending on the Policy Area under consideration. Therefore, after providing an overview of the BDM methodology, this paper aims to investigate two Policy Areas that have advanced at different paces over the same period of time, namely Policy Area 3 – Foster digital empowerment and digital literacy and Policy Area 4 – Strengthen trust through security in the digital sphere, to better understand how the Berlin Declaration and its related monitoring mechanism have helped Member States to foster the digital transformation of their governments and public services. To do so, good practices from different European countries, as well as initiatives advanced by the European Commission, will be examined. In its conclusion, this paper will point out some limitations related to the BDM itself and set forth possible future developments beyond the mere application of the Declaration, taking into account the priorities of the Belgian Presidency of the Council of the European Union, which will run from January to June 2024.
While the cutting-edge AI algorithms like ChatGPT are being adopted to government bureaucracy, there is a lack of discussion about which AI systems should be adopted and under what administrative conditions to ensure accountability. This study addresses the issue of AI system adoption in public sector from the perspective of accountability, which derived from institutionalism and democratic governance. Specifically, we borrow two terms from Romzek & Ingraham (2000) [36], "source of control" and "discretion of agency", to present a framework that categorizes "who can hold governments accountable" and "what kind of AI systems should be utilized?" for four types of accountability. we also point out the issue of the states of knowledge for each type of accountability and examines how AI systems can be adopted to address it. AI systems are discussed in terms of Symbolic and Connectionist AI systems, based on two approaches on AI. By doing so, we present a framework to help ensure that AI systems being deployed in the public sector are accountable.
